Journal
MOLECULAR CANCER THERAPEUTICS
Volume 20, Issue 5, Pages 896-905Publisher
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1535-7163.MCT-20-0183
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Choosing the right target for ADCs is crucial to reduce on-target toxicity. NaPi2b shows promising results in various human tumors, as demonstrated by XMT-1536. The development of a new IHC reagent helps to improve treatment efficacy.
Target selection for antibody-drug conjugates (ADC) frequently focuses on identifying antigens with differential expression in tumor and normal tissue, to mitigate the risk of on-target toxicity. However, this strategy restricts the possible target space. SLC34A2/NaPi2b is a sodium phosphate transporter expressed in a variety of human tumors including lung and ovarian carcinoma, as well as the normal tissues from which these tumors arise. Previous clinical trials with a NaPi2b targeting MMAE-ADCs have shown objective durable responses. However, the protein-based biomarker assay developed for use in that study was unable to discern a statistically significant relationship between NaPi2b protein expression and the probability of response. XMT-1536 is a NaPi2b targeting ADC comprised of a unique humanized antibody conjugated with 10-15 auristatin F-hydroxypropylamide (AF-HPA) payload molecules via the Dolaflexin platform. AF-HPA is a cell-permeable, antimitotic compound that is slowly metabolized intratumorally to an active, very low-permeable metabolite, auristatin F (AF), resulting in controlled bystander killing. We describe the preclinical in vitro and in vivo antitumor effects of XMT-1536 in models of ovarian and lung adenocarcinoma. Pharmacokinetic analysis showed approximately proportional increases in exposure in rat and monkey. Systemic free AF-HPA and AF concentrations were observed to be low in all animal species. Finally, we describe a unique IHC reagent, generated from a chimeric construct of the therapeutic antibody, that was used to derive a target expression and efficacy relationship in a series of ovarian primary xenograft cancer models.
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